class_docstr: Compute precision-recall pairs for different probability thresholds for binary classification task
  by accumulating predictions and the ground-truth during an epoch and applying
  `sklearn.metrics.precision_recall_curve <https://scikit-learn.org/stable/modules/generated/
  sklearn.metrics.precision_recall_curve.html#sklearn.metrics.precision_recall_curve>`_ .
  
  Args:
      output_transform: a callable that is used to transform the
          :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
          form expected by the metric. This can be useful if, for example, you have a multi-output model and
          you want to compute the metric with respect to one of the outputs.
      check_compute_fn: Default False. If True, `precision_recall_curve
          <https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_curve.html
          #sklearn.metrics.precision_recall_curve>`_ is run on the first batch of data to ensure there are
          no issues. User will be warned in case there are any issues computing the function.
  
  Note:
      PrecisionRecallCurve expects y to be comprised of 0's and 1's. y_pred must either be probability estimates
      or confidence values. To apply an activation to y_pred, use output_transform as shown below:
  
      .. code-block:: python
  
          def sigmoid_output_transform(output):
              y_pred, y = output
              y_pred = torch.sigmoid(y_pred)
              return y_pred, y
          avg_precision = PrecisionRecallCurve(sigmoid_output_transform)
  
  Examples:
  
      .. include:: defaults.rst
          :start-after: :orphan:
  
      .. testcode::
  
          y_pred = torch.tensor([0.0474, 0.5987, 0.7109, 0.9997])
          y_true = torch.tensor([0, 0, 1, 1])
          prec_recall_curve = PrecisionRecallCurve()
          prec_recall_curve.attach(default_evaluator, 'prec_recall_curve')
          state = default_evaluator.run([[y_pred, y_true]])
  
          print("Precision", [round(i, 4) for i in state.metrics['prec_recall_curve'][0].tolist()])
          print("Recall", [round(i, 4) for i in state.metrics['prec_recall_curve'][1].tolist()])
          print("Thresholds", [round(i, 4) for i in state.metrics['prec_recall_curve'][2].tolist()])
  
      .. testoutput::
  
          Precision [0.5, 0.6667, 1.0, 1.0, 1.0]
          Recall [1.0, 1.0, 1.0, 0.5, 0.0]
          Thresholds [0.0474, 0.5987, 0.7109, 0.9997]
code: def __init__(
      self,
      output_transform: Callable = lambda x: x,
      check_compute_fn: bool = False,
      device: Union[str, torch.device] = torch.device("cpu"),
  ) -> None:
      super(PrecisionRecallCurve, self).__init__(
          precision_recall_curve_compute_fn,  # type: ignore[arg-type]
          output_transform=output_transform,
          check_compute_fn=check_compute_fn,
          device=device,
      )
  
file_path: ignite/ignite/contrib/metrics/precision_recall_curve.py
imports: from typing import Any, Callable, cast, Tuple, Union
  import torch
  import ignite.distributed
  from ignite.exceptions import NotComputableError
  from ignite.metrics import EpochMetric
label_desc: Use image node_id 1 to create a new PrecisionRecallCurve object from inherited base classes: EpochMetric with example: obj = PrecisionRecallCurve(output_transform, check_compute_fn, device)